Hyperspectral Image Classification Using Deep Learning Technique

  • Mayar A. ShafaeyEmail author
  • Mohammed A.-M. Salem
  • Maryam N. Al-Berry
  • Hala M. Ebied
  • Elsayed A. El-Dahshan
  • Mohammed F. Tolba
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1153)


Today, the classification process is demanded for modern city planning, agriculture and environmental monitoring, and many other applications. The optimum classification degree is still insufficient so far. The present classification methods for remote-sensing images are grouped according to the features they use into: manual feature-based methods, unsupervised feature learning methods, and supervised feature learning methods. In recent times, the supervised deep learning approaches are extensively introduced in various remote-sensing applications, such as object detection and land use scene classification. In this article, an experiment is conducted using one of the widespread deep learning models, Convolution Neural Networks (CNNs), specifically, AlexNet architecture on a standard sounded hyper spectral dataset, Pavia University (PaviaU). The model achieved an overall accuracy of 91% ± 0.01. A comparison with other different techniques is also introduced.


Deep learning Convolution Neural Networks (CNNs) Remote-sensing Satellite images Hyperspectral images 


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Faculty of Computers and Information SciencesAin Shams UniversityCairoEgypt
  2. 2.Faculty of Media Engineering and TechnologyGerman University in CairoCairoEgypt
  3. 3.Faculty of Science, Department of PhysicsAin Shams UniversityCairoEgypt
  4. 4.National Egyptian E-Learning University (EELU)GizaEgypt

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